Cadaverine Suppresses Persistence to Carboxypenicillins in <i>Pseudomonas aeruginosa</i> PAO1
Bibliographic record
Abstract
The refractory nature of Pseudomonas aeruginosa infections is due in part to the presence of specialized cells, termed persisters, within the population. To identify genes involved in P. aeruginosa persister formation, a PAO1 transposon (Tn) library was challenged en masse with 1,000 μg/ml of carbenicillin and was enriched for mutants that were able to survive in the presence of this antibiotic. For one mutant that was further characterized, the carbenicillin MIC was equal to that of PAO1, but persister formation exhibited a 20-fold increase after exposure to the antibiotic. Sequence analysis revealed that the Tn had inserted into PA4115, a gene encoding a putative lysine decarboxylase. A PA4115 mutant that produced 48-fold and 20-fold more survivors than PAO1 after 10-h exposures to carbenicillin and ticarcillin, respectively, was generated by allelic exchange. Furthermore, the rate of carboxypenicillin-induced lysis was reduced in the PA4115 mutant. Under certain pH conditions, lysine decarboxylase converts lysine to cadaverine. By measuring cadaverine production, we discovered that the PA4115 mutant had significantly reduced lysine decarboxylase activity. To determine if reduced cadaverine levels are responsible for the increase in carbenicillin and ticarcillin persistence, viability and lysis assays were performed in the presence of exogenous cadaverine. Cadaverine increased the rate of killing and lysis of the PA4115 mutant in the presence of both antibiotics. These findings suggest that cadaverine may be able to enhance the effectiveness of carboxypenicillins against P. aeruginosa by reducing persister formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".